Resumen de Pláticas

Generative adversarial networks for graph dataDr. Daniel ZügnerResumen: Generative Adversarial Networks (GANs) have enjoyed much attention in the literature in recent years. This work applies GANs to a new domain: graph reconstruction. To tackle this problem, the generator simulates draws of random walks from the original graph, and the discriminator consequently aims to discriminate between real and fake (i.e. generated) random walks. We demonstrate that our model can (i) reconstruct a given graph with high accuracy (ii) recover edges from the graph that have previously been removed, proving that the model learns about structure in the graph. Furthermore, we show that we can smoothly change the generated graph’s properties by interpolating in latent space.